Feminisms in geography : rethinking space, place, and knowledges
Bibliographic record
Abstract
Editors: Pamela Moss and Karen Falconer al-Hindi, UNO faculty member. In this innovative reader, Pamela Moss and Karen Falconer Al-Hindi present a unique, reflective approach to what feminist geography is and who feminist geographers are. Their carefully crafted textbook invigorates feminist debates about space, place, and knowledges with a fine balance among teaching chapters, reprints, and original essays. Offering an anthology that actually questions the very purpose of an anthology, the editors create and then negotiate a tension between reinforcing and destabilizing scholarly authority. They challenge the idea that there is one set of works that acts as the vision, interpretation, voice, and feel of feminist geography while both reproducing key previously published works and including fresh essays from a number of feminist geographers in a single volume. The first chapter frames feminism, geography, and knowledge as a mélange of ideas, principles, and practices. Each of the three major sections of the volume begins with an introductory essay that places individual contributions into the overarching argument about the construction of feminist geography. Each introduction is then followed by a combination of reprints and original essays that contribute both to understanding how feminist geographical knowledge is constructed differently in different places and to showing what feminist geographers do wherever they are. The final chapter extends the anti-anthology arguments and raises questions that feminisms in geographies have yet to address. Students and scholars will find both the approach and the discussion essential for a full and nuanced understanding of feminist geography.Contributions by: Sybille Bauriedl, Kath Browne, Joos Droogleever Fortuijn, Kim England, Karen Falconer Al-Hindi, Anne-Françoise Gilbert, Melissa R. Gilbert, Ellen Hansen, Susan Hanson, Audrey Kobayashi, Clare Madge, Michele Masucci, Janice Monk, Pamela Moss, Ann M. Oberhauser, Linda Peake, Geraldine Pratt, Parvati Raghuram, Bernadette Stiell, Amy Trauger, Dina Vaiou, The Sangtin Writers: Anupamlata, Ramsheela, Reshma Ansari, Vibha Bajpayee, Shashi Vaish, Shashibala, Surbala, Richa Singh, and Richa Nagar
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.006 | 0.061 |
| Scholarly communication | 0.013 | 0.022 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".